In the recent decades, antibacterial peptides have occupied a strategic position for pharmaceutical drug applications and became subject of intense research activities since they are used to strengthen the immune system of all living organisms by protecting them from pathogenic bacteria. This work proposes a simple and easy statistical/computational method through a peptide polarity index measure by which an antibacterial peptide subgroup can be efficiently identified, that is, characterized by a high toxicity to bacterial membranes but presents a low toxicity to mammal cells. These peptides also have the feature not to adopt to an alpha-helicoidal structure in aqueous solution. The double-blind test carried out to the whole Antimicrobial Peptide Database (November 2011) showed an accuracy of 90% applying the polarity index method for the identification of such antibacterial peptide groups.

Antibacterial peptides are researched mainly for the potential benefit they have in a variety of socially relevant diseases, used by the host to protect itself from different types of pathogenic bacteria. We used the mathematical-computational method known as Hidden Markov models (HMMs) in targeting a subset of antibacterial peptides named Selective Cationic Amphipatic Antibacterial Peptides (SCAAPs). The main difference in the implementation of HMMs was focused on the detection of SCAAP using principally five physical-chemical properties for each candidate SCAAPs, instead of using the statistical information about the amino acids which form a peptide. By this method a cluster of antibacterial peptides was detected and as a result the following were found: 9 SCAAPs, 6 synthetic antibacterial peptides that belong to a subregion of Cecropin A and Magainin 2, and 19 peptides from the Cecropin A family. A scoring function was developed using HMMs as its core, uniquely employing information accessible from the databases.

This paper presents a mathematical-computational toy model based on the assumed dynamic principles of prebiotic peptide evolution. Starting from a pool of amino acid monomers, the model describes in a generalized manner the generation of peptides and their sequential information. The model integrates the intrinsic and dynamic key elements of the initiation of biopolymerization, such as the relative amino acid abundances and polarities, as well as the oligomer reversibility, i.e. fragmentation and recombination, and peptide self-replication. Our modeling results suggest that the relative amino acid abundances, as indicated by Miller-Urey type electric discharge experiments, played a principal role in the early sequential information of peptide profiles. Moreover, the computed profiles display an astonishing similarity to peptide profiles observed in so-called biological common ancestors found in the following three microorganisms; E. coli, M. jannaschii, and S. cereviasiae. The prebiotic peptide fingerprint was obtained by the so-called polarity index method that was earlier reported as a tool for the identification of cationic amphipathic antibacterial short peptides.

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.